HLM_summary {bruceR} | R Documentation |

## Tidy report of HLM (`lmer`

and `glmer`

models).

### Description

NOTE: `model_summary`

is preferred.

### Usage

```
HLM_summary(model = NULL, test.rand = FALSE, digits = 3, ...)
```

### Arguments

`model` |
A model fitted with |

`test.rand` |
[Only for |

`digits` |
Number of decimal places of output. Defaults to |

`...` |
Other arguments. You may re-define |

### Value

No return value.

### References

Hox, J. J. (2010).
*Multilevel analysis: Techniques and applications* (2nd ed.).
New York, NY: Routledge.

Nakagawa, S., & Schielzeth, H. (2013).
A general and simple method for obtaining *R*^2 from generalized linear mixed-effects models.
*Methods in Ecology and Evolution, 4,* 133–142.

Xu, R. (2003).
Measuring explained variation in linear mixed effects models.
*Statistics in Medicine, 22,* 3527–3541.

### See Also

`print_table`

(print simple table)

`model_summary`

(highly suggested)

### Examples

```
library(lmerTest)
## Example 1: data from lme4::sleepstudy
# (1) 'Subject' is a grouping/clustering variable
# (2) 'Days' is a level-1 predictor nested within 'Subject'
# (3) No level-2 predictors
m1 = lmer(Reaction ~ (1 | Subject), data=sleepstudy)
m2 = lmer(Reaction ~ Days + (1 | Subject), data=sleepstudy)
m3 = lmer(Reaction ~ Days + (Days | Subject), data=sleepstudy)
HLM_summary(m1)
HLM_summary(m2)
HLM_summary(m3)
## Example 2: data from lmerTest::carrots
# (1) 'Consumer' is a grouping/clustering variable
# (2) 'Sweetness' is a level-1 predictor
# (3) 'Age' and 'Frequency' are level-2 predictors
hlm.1 = lmer(Preference ~ Sweetness + Age + Frequency +
(1 | Consumer), data=carrots)
hlm.2 = lmer(Preference ~ Sweetness + Age + Frequency +
(Sweetness | Consumer) + (1 | Product), data=carrots)
HLM_summary(hlm.1)
HLM_summary(hlm.2)
```

*bruceR*version 2023.9 Index]